A bayesian approach for sensor optimisation in impact identification

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Title: A bayesian approach for sensor optimisation in impact identification
Author(s): Mallardo, V
Sharif Khodaei, Z
Aliabadi, MH
Item Type: Journal Article
Abstract: This paper presents a Bayesian approach for optimizing the position of sensors aimed at impact identification in composite structures under operational conditions. The uncertainty in the sensor data has been represented by statistical distributions of the recorded signals. An optimisation strategy based on the genetic algorithm is proposed to find the best sensor combination aimed at locating impacts on composite structures. A Bayesian-based objective function is adopted in the optimisation procedure as an indicator of the performance of meta-models developed for different sensor combinations to locate various impact events. To represent a real structure under operational load and to increase the reliability of the Structural Health Monitoring (SHM) system, the probability of malfunctioning sensors is included in the optimisation. The reliability and the robustness of the procedure is tested with experimental and numerical examples. Finally, the proposed optimisation algorithm is applied to a composite stiffened panel for both the uniform and non-uniform probability of impact occurrence.
Publication Date: 22-Nov-2016
Date of Acceptance: 9-Nov-2016
URI: http://hdl.handle.net/10044/1/42631
DOI: https://dx.doi.org/10.3390/ma9110946
ISSN: 1996-1944
Publisher: MDPI
Journal / Book Title: Materials
Volume: 9
Issue: 11
Copyright Statement: c 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: 03 Chemical Sciences
09 Engineering
Publication Status: Published
Article Number: ARTN 946
Appears in Collections:Faculty of Engineering

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